REAP supplemental fertilizer improves greenhouse crop yield
Bibliographic record
Abstract
Abstract Background Mine tailings contain rare earth elements, including lanthanum and cerium, and plant micronutrients including iron. Previous studies have demonstrated that fertilizers containing rare earth elements and/or micronutrients can influence plant physiology, nutrient uptake and crop yield. However, applying the right dose of these fertilizers is critical since the concentration range associated with benefits is often narrow, and overapplication can lead to crop yield reductions. This study aimed to quantify the effects of a water-soluble fertilizer, REAP, on the yield of greenhouse crops. Methods In the first experiment, the effects of three concentrations of REAP (100, 250 or 500 ppm) were compared to a control (0 ppm REAP) on growth of lettuce, tomato and pepper growing in soilless media. In the second experiments, the effects of REAP applied at higher rates (500, 1000 and 2000 ppm) were compared to a control (0 ppm REAP) on the growth of lettuce, peppers, tomato and cantaloupe. Results In the first experiment, there were no significant differences in yield between treatments, REAP appeared to promote root development. In the second experiment, there were significant yield increases for all crops treated with REAP. Gas exchange rates and nutrient concentration of tomato plants receiving REAP were not significantly different from the control. These results demonstrated that nutrient elements in REAP, including lanthanum, cerium, and micronutrients, improved the growth and yield of vegetable crops when applied at rates ranging from 500 to 2000 ppm.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".